Enseigné par

Google Cloud Training

Transcription

Welcome back to the data to insights specialization. I'm Evan and in this fourth course so far, it has one of the hottest topics of today which is applying Machine Learning to your data sets. Now for this course, you'll be building on your knowledge of SQL, Pre-processing, and BigQuery that you've learned so far in this specialization. So let's take a look at the topics that we're going to cover as part of this ML course. First, we'll explore the difference between Machine Learning, Artificial Intelligence, and Deep Learning as it relates to modern applications. These terms get mentioned a lot and having a clear understanding of when to use each, will serve you well. Then we'll explore some real-world applications for ML for businesses and highlight a few existing customer use cases for ML on Google Cloud Platform. After that, we'll discuss some key ML terms to know like instances, labels, features, and models. Then, we'll discuss my three secrets for ML. As a hint, they all have to do with good data. After that we'll talk about ML tool specifics and which tools that you'll be using as part of this course and for your labs. We'll then get to learn and practice invoking some pre-trained ML APIs for common tasks like image recognition and sentiment analysis. After that is how to create ML already datasets inside of BigQuery. Lastly, we'll explore and practice how to create and run ML models like forecasting models and classification models right within BigQuery. Does that sounds exciting? So let's get started.